166 citations · 293 across the 18 of their papers we have counts for
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deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks
Dennis Ulmer, Christian Hardmeier, Jes Frellsen
A lot of Machine Learning (ML) and Deep Learning (DL) research is of an empirical nature. Nevertheless, statistical significance testing (SST) is still not widely used. This endang…
Experimental Standards for Deep Learning in Natural Language Processing Research
Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein +5
The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, compared to more…
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation
Dennis Ulmer, Christian Hardmeier, Jes Frellsen
Popular approaches for quantifying predictive uncertainty in deep neural networks often involve distributions over weights or multiple models, for instance via Markov Chain samplin…